The relationship between major quality tools such as quality function development （QFD）, failure mode and effects analysis （FMEA）, design of experiments （DOE） and statistical process control （SPC） is analyzed ...The relationship between major quality tools such as quality function development （QFD）, failure mode and effects analysis （FMEA）, design of experiments （DOE） and statistical process control （SPC） is analyzed through an extensive review of the literature and the concurrent quality engineering philosophy, and a basic structure for the integration of quality tools is presented. An integrated quality management system （IQMS） is developed using C＋＋ Builder, running in the Windows 2000 Server environment with the basic internet connections, and SQL Server 2000 as the platform for developing the database, An illustrative example applying IQMS to the continuous quality improvement for a crane equipment manufacturing is reported. The result shows that the application of IQMS can optimize the process of design and manufacturing, shorten the cycle time of product, reduce the cost, and realize quality improvement continuously, The proposed integrated framework with IOMS is believed to be applicable to continuous quality improvement in many manufacturing companies.展开更多
The state-of-the-art universal steganalysis method,spatial rich model(SRM),and the steganalysis method using image quality metrics(IQM)are both based on image residuals,while they use 34671 and 10 features respectivel...The state-of-the-art universal steganalysis method,spatial rich model(SRM),and the steganalysis method using image quality metrics(IQM)are both based on image residuals,while they use 34671 and 10 features respectively.This paper proposes a novel steganalysis scheme that combines their advantages in two ways.First,filters used in the IQM are designed according to the models of the SRM owning to their strong abilities for detecting the content adaptive steganographic methods.In addition,a total variant(TV)filter is also used due to its good performance of preserving image edge properties during filtering.Second,due to each type of these filters having own advantages,the multiple filters are used simultaneously and the features extracted from their outputs are combined together.The whole steganalysis procedure is removing steganographic noise using those filters,then measuring the distances between images and their filtered version with the image quality metrics,and last feeding these metrics as features to build a steganalyzer using either an ensemble classifier or a support vector machine.The scheme can work in two modes,the single filter mode using 9 features,and the multi-filter mode using 639 features.We compared the performance of the proposed method,the SRM and the maxSRMd2.The maxSRMd2 is the improved version of the SRM.The simulated results show that the proposed method that worked in the multi-filter mode was about 10%more accurate than the SRM and maxSRMd2 when the data were globally normalized,and had similar performance with the SRM and maxSRMd2 when the data were locally normalized.展开更多
基金This project is supported by National Natural Science Foundation of China (No.70372062)Tianjin City Key Technologies R&D Program(No.04310881R)New Century Excellent Talent Program of Education Ministry of China(No.NCET-04-0240).
文摘The relationship between major quality tools such as quality function development （QFD）, failure mode and effects analysis （FMEA）, design of experiments （DOE） and statistical process control （SPC） is analyzed through an extensive review of the literature and the concurrent quality engineering philosophy, and a basic structure for the integration of quality tools is presented. An integrated quality management system （IQMS） is developed using C＋＋ Builder, running in the Windows 2000 Server environment with the basic internet connections, and SQL Server 2000 as the platform for developing the database, An illustrative example applying IQMS to the continuous quality improvement for a crane equipment manufacturing is reported. The result shows that the application of IQMS can optimize the process of design and manufacturing, shorten the cycle time of product, reduce the cost, and realize quality improvement continuously, The proposed integrated framework with IOMS is believed to be applicable to continuous quality improvement in many manufacturing companies.
基金This research was supported by National Natural Science Foundation of China(Grant Nos.41661144039,91337102,41401481)and Natural Science Foundation of Jiangsu Province of China(Grant No.BK20140997).
文摘The state-of-the-art universal steganalysis method,spatial rich model(SRM),and the steganalysis method using image quality metrics(IQM)are both based on image residuals,while they use 34671 and 10 features respectively.This paper proposes a novel steganalysis scheme that combines their advantages in two ways.First,filters used in the IQM are designed according to the models of the SRM owning to their strong abilities for detecting the content adaptive steganographic methods.In addition,a total variant(TV)filter is also used due to its good performance of preserving image edge properties during filtering.Second,due to each type of these filters having own advantages,the multiple filters are used simultaneously and the features extracted from their outputs are combined together.The whole steganalysis procedure is removing steganographic noise using those filters,then measuring the distances between images and their filtered version with the image quality metrics,and last feeding these metrics as features to build a steganalyzer using either an ensemble classifier or a support vector machine.The scheme can work in two modes,the single filter mode using 9 features,and the multi-filter mode using 639 features.We compared the performance of the proposed method,the SRM and the maxSRMd2.The maxSRMd2 is the improved version of the SRM.The simulated results show that the proposed method that worked in the multi-filter mode was about 10%more accurate than the SRM and maxSRMd2 when the data were globally normalized,and had similar performance with the SRM and maxSRMd2 when the data were locally normalized.
